Proposed computed tomography severity index for the evaluation of invasive fungal sinusitis: Preliminary results

Invasive fungal sinusitis (IFS) can present as a mild disease to life-threatening infection. A recent surge in cases was seen due to the coronavirus disease 2019 (COVID-19) pandemic. Many patients require surgical debridement and hence imaging [contrast-enhanced computed tomography (CECT) of the paranasal sinuses (PNS)] to document the extent of the disease. However, there was no scoring system using CECT to describe the severity of IFS. This study proposes a computed tomography (CT) severity index (CTSI) to describe the severity of rhino-orbital-cerebral involvement in symptomatic COVID-19 patients and hypothesizes that higher CTSI correlates with disease severity and thus slow response/non-response to treatment.

To propose a scoring system using CECT to describe the severity of IFS and correlate it with clinical outcomes.

A prospective study on 66 COVID-19 positive patients with CECT PNS done for IFS was performed. Split-bolus single-phase CT technique was used. Based on the extent of involvement, a CTSI was designed. Disease in four major subsite areas was assessed. Each subsite involvement was given points according to this model and then summated. Based on the final summated CTSI, the disease was classified as mild, moderate, or severe. Two subsets were subsequently analyzed including survival and death; and responders and non-responders.

The study cohort was 66 COVID-19-positive patients with suspected IFS with a median age of 48.5 years. Mild disease was noted in 34 (51.52%), moderate in 28 (42.42%), and severe disease in 4 (6.06%) patients. There was a significant association of mortality and poor clinical response (P = 0.02) with disease bilaterality. Laterality and CTSI were significant predictors of response to treatment. The mean CTSI of responders was 6.3, of non-responders was 12.9 and the response to treatment was significantly associated with CTSI (t-test, P < 0.001). Receiver operating characteristic curve analysis (Liu method) to distinguish between responders and non-responders showed that the cut-off value for CTSI of 11 had a sensitivity of 78.26% and a specificity of 95.35% to predict response assessment.

CTSI can help in quantification of the disease burden, mapping out disease extent, triaging patients, and response assessment; especially patients with underlying comorbidities. A higher score would alert the clinician to initiate aggressive treatment, as severe disease correlates with slow response/non-response to the treatment.